Overview

Dataset statistics

Number of variables18
Number of observations177049
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.3 MiB
Average record size in memory144.0 B

Variable types

Numeric12
Categorical6

Alerts

track_id has a high cardinality: 176774 distinct values High cardinality
artist_name has a high cardinality: 14564 distinct values High cardinality
track_name has a high cardinality: 148615 distinct values High cardinality
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
danceability is highly correlated with valenceHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
instrumentalness is highly correlated with loudnessHigh correlation
liveness is highly correlated with speechinessHigh correlation
loudness is highly correlated with acousticness and 2 other fieldsHigh correlation
speechiness is highly correlated with livenessHigh correlation
valence is highly correlated with danceabilityHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
danceability is highly correlated with valenceHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with acousticness and 1 other fieldsHigh correlation
valence is highly correlated with danceabilityHigh correlation
df_index is highly correlated with popularity and 1 other fieldsHigh correlation
popularity is highly correlated with df_indexHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
danceability is highly correlated with energy and 2 other fieldsHigh correlation
energy is highly correlated with acousticness and 3 other fieldsHigh correlation
liveness is highly correlated with speechinessHigh correlation
loudness is highly correlated with acousticness and 3 other fieldsHigh correlation
speechiness is highly correlated with df_index and 1 other fieldsHigh correlation
valence is highly correlated with danceability and 2 other fieldsHigh correlation
track_id is uniformly distributed Uniform
track_name is uniformly distributed Uniform
df_index has unique values Unique
popularity has 6126 (3.5%) zeros Zeros
instrumentalness has 58286 (32.9%) zeros Zeros

Reproduction

Analysis started2021-12-04 18:38:02.920855
Analysis finished2021-12-04 18:38:34.272842
Duration31.35 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct177049
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95551.4174
Minimum0
Maximum191053
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-12-04T12:38:34.676845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8896.4
Q145612
median98099
Q3144608
95-th percentile181307.2
Maximum191053
Range191053
Interquartile range (IQR)98996

Descriptive statistics

Standard deviation56025.29283
Coefficient of variation (CV)0.5863365961
Kurtosis-1.257950236
Mean95551.4174
Median Absolute Deviation (MAD)49503
Skewness-0.02170866302
Sum1.69172829 × 1010
Variance3138833437
MonotonicityStrictly increasing
2021-12-04T12:38:34.769843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
1298021
 
< 0.1%
1297941
 
< 0.1%
1297951
 
< 0.1%
1297961
 
< 0.1%
1297971
 
< 0.1%
1297981
 
< 0.1%
1297991
 
< 0.1%
1298001
 
< 0.1%
1298011
 
< 0.1%
Other values (177039)177039
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
1910531
< 0.1%
1910511
< 0.1%
1910501
< 0.1%
1910481
< 0.1%
1910471
< 0.1%
1910461
< 0.1%
1910451
< 0.1%
1910441
< 0.1%
1910431
< 0.1%
1910421
< 0.1%

track_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct176774
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
6TNNMVpOgn8K5NoDC7alG6
 
2
0NX14YH2t16bwwlJSfXazr
 
2
2zbV4xRYLuElz4PWOXI5P7
 
2
59h3i22MBicerNR1llNXqv
 
2
3famfyGuWw5QFcVO5Xk1uW
 
2
Other values (176769)
177039 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3895078
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176499 ?
Unique (%)99.7%

Sample

1st row0BRjO6ga9RKCKjfDqeFgWV
2nd row0BjC1NfoEOOusryehmNudP
3rd row0CoSDzoNIKCRs124s9uTVy
4th row0Gc6TVm52BwZD07Ki6tIvf
5th row0IuslXpMROHdEPvSl1fTQK

Common Values

ValueCountFrequency (%)
6TNNMVpOgn8K5NoDC7alG62
 
< 0.1%
0NX14YH2t16bwwlJSfXazr2
 
< 0.1%
2zbV4xRYLuElz4PWOXI5P72
 
< 0.1%
59h3i22MBicerNR1llNXqv2
 
< 0.1%
3famfyGuWw5QFcVO5Xk1uW2
 
< 0.1%
6LlulaQFzRuypfDLBPCWUq2
 
< 0.1%
7hQ0ojbeqicGhw0wdUVeaN2
 
< 0.1%
4EYpOdL69yWuIRc49AyeZa2
 
< 0.1%
3d9DChrdc6BOeFsbrZ3Is02
 
< 0.1%
0d28khcov6AiegSCpG5TuT2
 
< 0.1%
Other values (176764)177029
> 99.9%

Length

2021-12-04T12:38:34.877842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6tnnmvpogn8k5nodc7alg62
 
< 0.1%
77waxok6i6gtjiaswrsatj2
 
< 0.1%
4nqxapv6zag9lccf1qftvg2
 
< 0.1%
60ikvf7ufqxmt5cwkpcx8a2
 
< 0.1%
6ya1lnvi98lxumtmks3fik2
 
< 0.1%
0cg9ocyqyoivtgiwtmte3g2
 
< 0.1%
7gce7k2ilprzxzmcfwttj92
 
< 0.1%
0tqbbrdwai3zdm90tmyt8j2
 
< 0.1%
0noh9m0tigapprje5ovauc2
 
< 0.1%
7mv9vh3bbuc1hgvlq6uwco2
 
< 0.1%
Other values (176764)177029
> 99.9%

Most occurring characters

ValueCountFrequency (%)
182947
 
2.1%
082912
 
2.1%
582780
 
2.1%
382771
 
2.1%
282739
 
2.1%
682735
 
2.1%
482600
 
2.1%
778318
 
2.0%
s60493
 
1.6%
860445
 
1.6%
Other values (52)3116338
80.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1559789
40.0%
Uppercase Letter1556947
40.0%
Decimal Number778342
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s60493
 
3.9%
m60389
 
3.9%
n60349
 
3.9%
y60341
 
3.9%
r60300
 
3.9%
j60284
 
3.9%
c60235
 
3.9%
h60184
 
3.9%
u60115
 
3.9%
i60101
 
3.9%
Other values (16)956998
61.4%
Uppercase Letter
ValueCountFrequency (%)
L60372
 
3.9%
B60338
 
3.9%
I60329
 
3.9%
M60279
 
3.9%
Q60171
 
3.9%
O60144
 
3.9%
H60132
 
3.9%
P60118
 
3.9%
C60073
 
3.9%
T59988
 
3.9%
Other values (16)955003
61.3%
Decimal Number
ValueCountFrequency (%)
182947
10.7%
082912
10.7%
582780
10.6%
382771
10.6%
282739
10.6%
682735
10.6%
482600
10.6%
778318
10.1%
860445
7.8%
960095
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin3116736
80.0%
Common778342
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s60493
 
1.9%
m60389
 
1.9%
L60372
 
1.9%
n60349
 
1.9%
y60341
 
1.9%
B60338
 
1.9%
I60329
 
1.9%
r60300
 
1.9%
j60284
 
1.9%
M60279
 
1.9%
Other values (42)2513262
80.6%
Common
ValueCountFrequency (%)
182947
10.7%
082912
10.7%
582780
10.6%
382771
10.6%
282739
10.6%
682735
10.6%
482600
10.6%
778318
10.1%
860445
7.8%
960095
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3895078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
182947
 
2.1%
082912
 
2.1%
582780
 
2.1%
382771
 
2.1%
282739
 
2.1%
682735
 
2.1%
482600
 
2.1%
778318
 
2.0%
s60493
 
1.6%
860445
 
1.6%
Other values (52)3116338
80.0%

artist_name
Categorical

HIGH CARDINALITY

Distinct14564
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Giuseppe Verdi
 
1312
Giacomo Puccini
 
1095
Kimbo Children's Music
 
971
Wolfgang Amadeus Mozart
 
800
Richard Wagner
 
778
Other values (14559)
172093 

Length

Max length84
Median length12
Mean length12.42028478
Min length1

Characters and Unicode

Total characters2198999
Distinct characters267
Distinct categories16 ?
Distinct scripts7 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4249 ?
Unique (%)2.4%

Sample

1st rowHenri Salvador
2nd rowMartin & les fées
3rd rowJoseph Williams
4th rowHenri Salvador
5th rowFabien Nataf

Common Values

ValueCountFrequency (%)
Giuseppe Verdi1312
 
0.7%
Giacomo Puccini1095
 
0.6%
Kimbo Children's Music971
 
0.5%
Wolfgang Amadeus Mozart800
 
0.5%
Richard Wagner778
 
0.4%
Nobuo Uematsu773
 
0.4%
Juice Music684
 
0.4%
Georges Bizet677
 
0.4%
Randy Newman667
 
0.4%
Johann Sebastian Bach632
 
0.4%
Other values (14554)168660
95.3%

Length

2021-12-04T12:38:34.972844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the13391
 
3.7%
4537
 
1.3%
music2897
 
0.8%
john2025
 
0.6%
giuseppe1364
 
0.4%
verdi1312
 
0.4%
of1294
 
0.4%
children's1197
 
0.3%
richard1156
 
0.3%
giacomo1141
 
0.3%
Other values (15108)332624
91.6%

Most occurring characters

ValueCountFrequency (%)
e188806
 
8.6%
185889
 
8.5%
a170482
 
7.8%
i138635
 
6.3%
n131948
 
6.0%
o130380
 
5.9%
r118134
 
5.4%
s91071
 
4.1%
l90845
 
4.1%
t77202
 
3.5%
Other values (257)875607
39.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1591657
72.4%
Uppercase Letter398314
 
18.1%
Space Separator185889
 
8.5%
Other Punctuation14147
 
0.6%
Decimal Number4268
 
0.2%
Dash Punctuation2919
 
0.1%
Other Letter657
 
< 0.1%
Currency Symbol560
 
< 0.1%
Math Symbol152
 
< 0.1%
Close Punctuation150
 
< 0.1%
Other values (6)286
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
43
 
6.5%
24
 
3.7%
24
 
3.7%
22
 
3.3%
22
 
3.3%
22
 
3.3%
22
 
3.3%
22
 
3.3%
22
 
3.3%
21
 
3.2%
Other values (98)413
62.9%
Lowercase Letter
ValueCountFrequency (%)
e188806
11.9%
a170482
10.7%
i138635
 
8.7%
n131948
 
8.3%
o130380
 
8.2%
r118134
 
7.4%
s91071
 
5.7%
l90845
 
5.7%
t77202
 
4.9%
h67139
 
4.2%
Other values (59)387015
24.3%
Uppercase Letter
ValueCountFrequency (%)
S30878
 
7.8%
M30220
 
7.6%
T29749
 
7.5%
B29191
 
7.3%
C27172
 
6.8%
A23013
 
5.8%
J20585
 
5.2%
D20226
 
5.1%
R19208
 
4.8%
G18123
 
4.5%
Other values (32)149949
37.6%
Other Punctuation
ValueCountFrequency (%)
&4245
30.0%
.4009
28.3%
'2994
21.2%
"1082
 
7.6%
!907
 
6.4%
,630
 
4.5%
/101
 
0.7%
*88
 
0.6%
:37
 
0.3%
?23
 
0.2%
Other values (6)31
 
0.2%
Decimal Number
ValueCountFrequency (%)
1954
22.4%
2624
14.6%
3519
12.2%
0404
9.5%
4384
9.0%
5372
 
8.7%
7339
 
7.9%
9239
 
5.6%
8219
 
5.1%
6214
 
5.0%
Other Symbol
ValueCountFrequency (%)
24
36.4%
21
31.8%
15
22.7%
3
 
4.5%
3
 
4.5%
Math Symbol
ValueCountFrequency (%)
+144
94.7%
×5
 
3.3%
=3
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
-2913
99.8%
6
 
0.2%
Close Punctuation
ValueCountFrequency (%)
]90
60.0%
)60
40.0%
Open Punctuation
ValueCountFrequency (%)
[90
60.0%
(60
40.0%
Final Punctuation
ValueCountFrequency (%)
43
87.8%
6
 
12.2%
Initial Punctuation
ValueCountFrequency (%)
6
85.7%
1
 
14.3%
Space Separator
ValueCountFrequency (%)
185889
100.0%
Currency Symbol
ValueCountFrequency (%)
$560
100.0%
Modifier Letter
ValueCountFrequency (%)
12
100.0%
Modifier Symbol
ValueCountFrequency (%)
^2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1989919
90.5%
Common208361
 
9.5%
Han421
 
< 0.1%
Hiragana201
 
< 0.1%
Greek55
 
< 0.1%
Hangul22
 
< 0.1%
Katakana20
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e188806
 
9.5%
a170482
 
8.6%
i138635
 
7.0%
n131948
 
6.6%
o130380
 
6.6%
r118134
 
5.9%
s91071
 
4.6%
l90845
 
4.6%
t77202
 
3.9%
h67139
 
3.4%
Other values (98)785277
39.5%
Han
ValueCountFrequency (%)
24
 
5.7%
24
 
5.7%
22
 
5.2%
21
 
5.0%
20
 
4.8%
20
 
4.8%
17
 
4.0%
16
 
3.8%
16
 
3.8%
16
 
3.8%
Other values (65)225
53.4%
Common
ValueCountFrequency (%)
185889
89.2%
&4245
 
2.0%
.4009
 
1.9%
'2994
 
1.4%
-2913
 
1.4%
"1082
 
0.5%
1954
 
0.5%
!907
 
0.4%
,630
 
0.3%
2624
 
0.3%
Other values (38)4114
 
2.0%
Katakana
ValueCountFrequency (%)
3
15.0%
2
10.0%
2
10.0%
2
10.0%
2
10.0%
2
10.0%
2
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (2)2
10.0%
Hiragana
ValueCountFrequency (%)
43
21.4%
22
10.9%
22
10.9%
22
10.9%
22
10.9%
22
10.9%
20
10.0%
13
 
6.5%
13
 
6.5%
1
 
0.5%
Hangul
ValueCountFrequency (%)
4
18.2%
4
18.2%
4
18.2%
4
18.2%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Greek
ValueCountFrequency (%)
μ47
85.5%
Δ4
 
7.3%
Π4
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2192115
99.7%
None6098
 
0.3%
CJK409
 
< 0.1%
Hiragana201
 
< 0.1%
Punctuation68
 
< 0.1%
Misc Symbols39
 
< 0.1%
Hangul22
 
< 0.1%
Geometric Shapes21
 
< 0.1%
Katakana20
 
< 0.1%
Dingbats6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e188806
 
8.6%
185889
 
8.5%
a170482
 
7.8%
i138635
 
6.3%
n131948
 
6.0%
o130380
 
5.9%
r118134
 
5.4%
s91071
 
4.2%
l90845
 
4.1%
t77202
 
3.5%
Other values (76)868723
39.6%
None
ValueCountFrequency (%)
é2889
47.4%
á570
 
9.3%
ó384
 
6.3%
ö372
 
6.1%
í351
 
5.8%
ñ151
 
2.5%
ř141
 
2.3%
ë95
 
1.6%
Ñ91
 
1.5%
è84
 
1.4%
Other values (53)970
 
15.9%
Punctuation
ValueCountFrequency (%)
43
63.2%
6
 
8.8%
6
 
8.8%
6
 
8.8%
6
 
8.8%
1
 
1.5%
Hiragana
ValueCountFrequency (%)
43
21.4%
22
10.9%
22
10.9%
22
10.9%
22
10.9%
22
10.9%
20
10.0%
13
 
6.5%
13
 
6.5%
1
 
0.5%
CJK
ValueCountFrequency (%)
24
 
5.9%
24
 
5.9%
22
 
5.4%
21
 
5.1%
20
 
4.9%
20
 
4.9%
17
 
4.2%
16
 
3.9%
16
 
3.9%
16
 
3.9%
Other values (64)213
52.1%
Misc Symbols
ValueCountFrequency (%)
24
61.5%
15
38.5%
Geometric Shapes
ValueCountFrequency (%)
21
100.0%
Hangul
ValueCountFrequency (%)
4
18.2%
4
18.2%
4
18.2%
4
18.2%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Katakana
ValueCountFrequency (%)
3
15.0%
2
10.0%
2
10.0%
2
10.0%
2
10.0%
2
10.0%
2
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (2)2
10.0%
Dingbats
ValueCountFrequency (%)
3
50.0%
3
50.0%

track_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct148615
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Home
 
68
Intro
 
60
Closer
 
40
Stay
 
39
Without You
 
39
Other values (148610)
176803 

Length

Max length292
Median length16
Mean length21.18563787
Min length1

Characters and Unicode

Total characters3750896
Distinct characters1735
Distinct categories21 ?
Distinct scripts10 ?
Distinct blocks17 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136047 ?
Unique (%)76.8%

Sample

1st rowC'est beau de faire un Show
2nd rowPerdu d'avance (par Gad Elmaleh)
3rd rowDon't Let Me Be Lonely Tonight
4th rowDis-moi Monsieur Gordon Cooper
5th rowOuverture

Common Values

ValueCountFrequency (%)
Home68
 
< 0.1%
Intro60
 
< 0.1%
Closer40
 
< 0.1%
Stay39
 
< 0.1%
Without You39
 
< 0.1%
You38
 
< 0.1%
Forever37
 
< 0.1%
Smile35
 
< 0.1%
Wake Up35
 
< 0.1%
Beautiful35
 
< 0.1%
Other values (148605)176623
99.8%

Length

2021-12-04T12:38:35.085843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
29480
 
4.2%
the21507
 
3.1%
in9422
 
1.4%
i8325
 
1.2%
you7689
 
1.1%
a7651
 
1.1%
of7131
 
1.0%
feat7040
 
1.0%
no6035
 
0.9%
me5901
 
0.8%
Other values (62786)587018
84.2%

Most occurring characters

ValueCountFrequency (%)
520150
 
13.9%
e330543
 
8.8%
o223946
 
6.0%
a219708
 
5.9%
i191102
 
5.1%
n184247
 
4.9%
r168054
 
4.5%
t166849
 
4.4%
s121128
 
3.2%
l118294
 
3.2%
Other values (1725)1506875
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2379371
63.4%
Uppercase Letter601425
 
16.0%
Space Separator520150
 
13.9%
Other Punctuation111858
 
3.0%
Decimal Number53879
 
1.4%
Dash Punctuation25565
 
0.7%
Close Punctuation20614
 
0.5%
Open Punctuation20599
 
0.5%
Other Letter15196
 
0.4%
Final Punctuation752
 
< 0.1%
Other values (11)1487
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
680
 
4.5%
390
 
2.6%
263
 
1.7%
247
 
1.6%
242
 
1.6%
239
 
1.6%
233
 
1.5%
215
 
1.4%
196
 
1.3%
195
 
1.3%
Other values (1449)12296
80.9%
Lowercase Letter
ValueCountFrequency (%)
e330543
13.9%
o223946
 
9.4%
a219708
 
9.2%
i191102
 
8.0%
n184247
 
7.7%
r168054
 
7.1%
t166849
 
7.0%
s121128
 
5.1%
l118294
 
5.0%
u84983
 
3.6%
Other values (97)570517
24.0%
Uppercase Letter
ValueCountFrequency (%)
S49595
 
8.2%
T47401
 
7.9%
M41826
 
7.0%
A41250
 
6.9%
I37829
 
6.3%
L37598
 
6.3%
B31759
 
5.3%
C30383
 
5.1%
D28059
 
4.7%
R27232
 
4.5%
Other values (55)228493
38.0%
Other Punctuation
ValueCountFrequency (%)
.29969
26.8%
,20650
18.5%
'17307
15.5%
:16335
14.6%
"11552
 
10.3%
/6534
 
5.8%
!3426
 
3.1%
&3222
 
2.9%
?1661
 
1.5%
*335
 
0.3%
Other values (14)867
 
0.8%
Other Symbol
ValueCountFrequency (%)
°26
32.1%
®13
16.0%
9
 
11.1%
8
 
9.9%
7
 
8.6%
5
 
6.2%
3
 
3.7%
2
 
2.5%
2
 
2.5%
2
 
2.5%
Other values (4)4
 
4.9%
Math Symbol
ValueCountFrequency (%)
+97
33.3%
~85
29.2%
|30
 
10.3%
19
 
6.5%
=17
 
5.8%
<14
 
4.8%
>12
 
4.1%
×11
 
3.8%
2
 
0.7%
2
 
0.7%
Other values (2)2
 
0.7%
Decimal Number
ValueCountFrequency (%)
111795
21.9%
29867
18.3%
07356
13.7%
34609
 
8.6%
94158
 
7.7%
43921
 
7.3%
63221
 
6.0%
53212
 
6.0%
72890
 
5.4%
82850
 
5.3%
Open Punctuation
ValueCountFrequency (%)
(19358
94.0%
[1149
 
5.6%
73
 
0.4%
10
 
< 0.1%
4
 
< 0.1%
2
 
< 0.1%
2
 
< 0.1%
{1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
)19366
93.9%
]1148
 
5.6%
80
 
0.4%
10
 
< 0.1%
5
 
< 0.1%
4
 
< 0.1%
}1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-25372
99.2%
97
 
0.4%
65
 
0.3%
31
 
0.1%
Final Punctuation
ValueCountFrequency (%)
646
85.9%
99
 
13.2%
»7
 
0.9%
Modifier Letter
ValueCountFrequency (%)
551
97.7%
12
 
2.1%
ͺ1
 
0.2%
Currency Symbol
ValueCountFrequency (%)
$238
98.8%
¥2
 
0.8%
£1
 
0.4%
Initial Punctuation
ValueCountFrequency (%)
122
80.8%
22
 
14.6%
«7
 
4.6%
Modifier Symbol
ValueCountFrequency (%)
´32
74.4%
`6
 
14.0%
^5
 
11.6%
Format
ValueCountFrequency (%)
15
62.5%
8
33.3%
­1
 
4.2%
Letter Number
ValueCountFrequency (%)
2
66.7%
1
33.3%
Other Number
ValueCountFrequency (%)
²2
50.0%
½2
50.0%
Space Separator
ValueCountFrequency (%)
520150
100.0%
Connector Punctuation
ValueCountFrequency (%)
_83
100.0%
Control
ValueCountFrequency (%)
’2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2980316
79.5%
Common754888
 
20.1%
Katakana5758
 
0.2%
Han4904
 
0.1%
Hiragana4083
 
0.1%
Cyrillic486
 
< 0.1%
Hangul442
 
< 0.1%
Arabic8
 
< 0.1%
Hebrew8
 
< 0.1%
Greek3
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
54
 
1.1%
53
 
1.1%
49
 
1.0%
46
 
0.9%
46
 
0.9%
44
 
0.9%
44
 
0.9%
39
 
0.8%
39
 
0.8%
38
 
0.8%
Other values (1090)4452
90.8%
Hangul
ValueCountFrequency (%)
12
 
2.7%
11
 
2.5%
10
 
2.3%
9
 
2.0%
9
 
2.0%
8
 
1.8%
8
 
1.8%
7
 
1.6%
7
 
1.6%
7
 
1.6%
Other values (181)354
80.1%
Latin
ValueCountFrequency (%)
e330543
 
11.1%
o223946
 
7.5%
a219708
 
7.4%
i191102
 
6.4%
n184247
 
6.2%
r168054
 
5.6%
t166849
 
5.6%
s121128
 
4.1%
l118294
 
4.0%
u84983
 
2.9%
Other values (117)1171462
39.3%
Common
ValueCountFrequency (%)
520150
68.9%
.29969
 
4.0%
-25372
 
3.4%
,20650
 
2.7%
)19366
 
2.6%
(19358
 
2.6%
'17307
 
2.3%
:16335
 
2.2%
111795
 
1.6%
"11552
 
1.5%
Other values (90)63034
 
8.4%
Katakana
ValueCountFrequency (%)
390
 
6.8%
263
 
4.6%
247
 
4.3%
239
 
4.2%
233
 
4.0%
215
 
3.7%
196
 
3.4%
164
 
2.8%
153
 
2.7%
137
 
2.4%
Other values (70)3521
61.1%
Hiragana
ValueCountFrequency (%)
680
 
16.7%
242
 
5.9%
195
 
4.8%
174
 
4.3%
174
 
4.3%
139
 
3.4%
125
 
3.1%
113
 
2.8%
106
 
2.6%
101
 
2.5%
Other values (64)2034
49.8%
Cyrillic
ValueCountFrequency (%)
о49
 
10.1%
а47
 
9.7%
н37
 
7.6%
и36
 
7.4%
с27
 
5.6%
т23
 
4.7%
е21
 
4.3%
к21
 
4.3%
р18
 
3.7%
л16
 
3.3%
Other values (36)191
39.3%
Arabic
ValueCountFrequency (%)
ن2
25.0%
ي1
12.5%
و1
12.5%
ر1
12.5%
ا1
12.5%
ل1
12.5%
ع1
12.5%
Hebrew
ValueCountFrequency (%)
ל2
25.0%
ב1
12.5%
ק1
12.5%
ר1
12.5%
י1
12.5%
ת1
12.5%
ו1
12.5%
Greek
ValueCountFrequency (%)
μ1
33.3%
θ1
33.3%
ͺ1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3723344
99.3%
None9954
 
0.3%
Katakana6475
 
0.2%
CJK4892
 
0.1%
Hiragana4083
 
0.1%
Punctuation1132
 
< 0.1%
Cyrillic486
 
< 0.1%
Hangul442
 
< 0.1%
Misc Symbols23
 
< 0.1%
Math Operators22
 
< 0.1%
Other values (7)43
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
520150
 
14.0%
e330543
 
8.9%
o223946
 
6.0%
a219708
 
5.9%
i191102
 
5.1%
n184247
 
4.9%
r168054
 
4.5%
t166849
 
4.5%
s121128
 
3.3%
l118294
 
3.2%
Other values (85)1479323
39.7%
None
ValueCountFrequency (%)
é2793
28.1%
è1079
 
10.8%
ó635
 
6.4%
ü582
 
5.8%
á562
 
5.6%
í478
 
4.8%
ä403
 
4.0%
à383
 
3.8%
ñ356
 
3.6%
ö317
 
3.2%
Other values (91)2366
23.8%
Hiragana
ValueCountFrequency (%)
680
 
16.7%
242
 
5.9%
195
 
4.8%
174
 
4.3%
174
 
4.3%
139
 
3.4%
125
 
3.1%
113
 
2.8%
106
 
2.6%
101
 
2.5%
Other values (64)2034
49.8%
Punctuation
ValueCountFrequency (%)
646
57.1%
122
 
10.8%
110
 
9.7%
99
 
8.7%
65
 
5.7%
31
 
2.7%
22
 
1.9%
15
 
1.3%
11
 
1.0%
8
 
0.7%
Other values (2)3
 
0.3%
Katakana
ValueCountFrequency (%)
551
 
8.5%
390
 
6.0%
263
 
4.1%
247
 
3.8%
239
 
3.7%
233
 
3.6%
215
 
3.3%
196
 
3.0%
166
 
2.6%
164
 
2.5%
Other values (72)3811
58.9%
CJK
ValueCountFrequency (%)
54
 
1.1%
53
 
1.1%
49
 
1.0%
46
 
0.9%
46
 
0.9%
44
 
0.9%
44
 
0.9%
39
 
0.8%
39
 
0.8%
38
 
0.8%
Other values (1089)4440
90.8%
Cyrillic
ValueCountFrequency (%)
о49
 
10.1%
а47
 
9.7%
н37
 
7.6%
и36
 
7.4%
с27
 
5.6%
т23
 
4.7%
е21
 
4.3%
к21
 
4.3%
р18
 
3.7%
л16
 
3.3%
Other values (36)191
39.3%
Math Operators
ValueCountFrequency (%)
19
86.4%
2
 
9.1%
1
 
4.5%
Hangul
ValueCountFrequency (%)
12
 
2.7%
11
 
2.5%
10
 
2.3%
9
 
2.0%
9
 
2.0%
8
 
1.8%
8
 
1.8%
7
 
1.6%
7
 
1.6%
7
 
1.6%
Other values (181)354
80.1%
Misc Symbols
ValueCountFrequency (%)
9
39.1%
5
21.7%
3
 
13.0%
2
 
8.7%
2
 
8.7%
1
 
4.3%
1
 
4.3%
Geometric Shapes
ValueCountFrequency (%)
8
44.4%
7
38.9%
2
 
11.1%
1
 
5.6%
Arabic
ValueCountFrequency (%)
ن2
25.0%
ي1
12.5%
و1
12.5%
ر1
12.5%
ا1
12.5%
ل1
12.5%
ع1
12.5%
Arrows
ValueCountFrequency (%)
2
66.7%
1
33.3%
Number Forms
ValueCountFrequency (%)
2
66.7%
1
33.3%
Hebrew
ValueCountFrequency (%)
ל2
25.0%
ב1
12.5%
ק1
12.5%
ר1
12.5%
י1
12.5%
ת1
12.5%
ו1
12.5%
IPA Ext
ValueCountFrequency (%)
ʇ1
50.0%
ə1
50.0%
Box Drawing
ValueCountFrequency (%)
1
100.0%

popularity
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct274
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.41445589
Minimum0
Maximum100
Zeros6126
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-12-04T12:38:35.191843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q125
median37
Q349
95-th percentile64
Maximum100
Range100
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.47655997
Coefficient of variation (CV)0.4799346728
Kurtosis-0.3672411913
Mean36.41445589
Median Absolute Deviation (MAD)12
Skewness-0.1301405532
Sum6447143
Variance305.4301484
MonotonicityNot monotonic
2021-12-04T12:38:35.292843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06126
 
3.5%
383902
 
2.2%
363849
 
2.2%
413846
 
2.2%
333834
 
2.2%
323801
 
2.1%
353794
 
2.1%
293759
 
2.1%
403753
 
2.1%
423741
 
2.1%
Other values (264)136644
77.2%
ValueCountFrequency (%)
06126
3.5%
0.533
 
< 0.1%
11271
 
0.7%
1.513
 
< 0.1%
2890
 
0.5%
2.52
 
< 0.1%
3800
 
0.5%
4802
 
0.5%
4.52
 
< 0.1%
5911
 
0.5%
ValueCountFrequency (%)
1001
 
< 0.1%
992
 
< 0.1%
981
 
< 0.1%
976
< 0.1%
963
< 0.1%
95.51
 
< 0.1%
956
< 0.1%
943
< 0.1%
93.51
 
< 0.1%
935
< 0.1%

acousticness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4734
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4038228438
Minimum0
Maximum0.996
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-12-04T12:38:35.393842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0008754
Q10.0454
median0.288
Q30.79
95-th percentile0.979
Maximum0.996
Range0.996
Interquartile range (IQR)0.7446

Descriptive statistics

Standard deviation0.3662487597
Coefficient of variation (CV)0.9069540401
Kurtosis-1.478329404
Mean0.4038228438
Median Absolute Deviation (MAD)0.27867
Skewness0.3677675868
Sum71496.43068
Variance0.134138154
MonotonicityNot monotonic
2021-12-04T12:38:35.490843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995813
 
0.5%
0.994673
 
0.4%
0.992640
 
0.4%
0.993622
 
0.4%
0.991566
 
0.3%
0.99549
 
0.3%
0.985490
 
0.3%
0.987487
 
0.3%
0.988474
 
0.3%
0.989472
 
0.3%
Other values (4724)171263
96.7%
ValueCountFrequency (%)
01
< 0.1%
1 × 10-61
< 0.1%
1.02 × 10-61
< 0.1%
1.08 × 10-61
< 0.1%
1.12 × 10-61
< 0.1%
1.18 × 10-61
< 0.1%
1.21 × 10-61
< 0.1%
1.27 × 10-61
< 0.1%
1.28 × 10-62
< 0.1%
1.3 × 10-61
< 0.1%
ValueCountFrequency (%)
0.996243
 
0.1%
0.995813
0.5%
0.994673
0.4%
0.993622
0.4%
0.992640
0.4%
0.991566
0.3%
0.99549
0.3%
0.989472
0.3%
0.988474
0.3%
0.987487
0.3%

danceability
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1295
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5411056668
Minimum0.0569
Maximum0.989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-12-04T12:38:35.583843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.0569
5-th percentile0.189
Q10.415
median0.558
Q30.683
95-th percentile0.827
Maximum0.989
Range0.9321
Interquartile range (IQR)0.268

Descriptive statistics

Standard deviation0.1903358562
Coefficient of variation (CV)0.3517535813
Kurtosis-0.4923983746
Mean0.5411056668
Median Absolute Deviation (MAD)0.133
Skewness-0.3321236548
Sum95802.2172
Variance0.03622773815
MonotonicityNot monotonic
2021-12-04T12:38:35.675842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.589401
 
0.2%
0.626401
 
0.2%
0.62401
 
0.2%
0.57398
 
0.2%
0.61395
 
0.2%
0.628394
 
0.2%
0.629393
 
0.2%
0.547391
 
0.2%
0.556391
 
0.2%
0.573390
 
0.2%
Other values (1285)173094
97.8%
ValueCountFrequency (%)
0.05691
 
< 0.1%
0.0571
 
< 0.1%
0.05721
 
< 0.1%
0.05731
 
< 0.1%
0.05771
 
< 0.1%
0.05812
< 0.1%
0.05821
 
< 0.1%
0.05841
 
< 0.1%
0.0591
 
< 0.1%
0.05923
< 0.1%
ValueCountFrequency (%)
0.9891
 
< 0.1%
0.9872
 
< 0.1%
0.9861
 
< 0.1%
0.9851
 
< 0.1%
0.9821
 
< 0.1%
0.9812
 
< 0.1%
0.985
< 0.1%
0.9793
< 0.1%
0.9785
< 0.1%
0.9772
 
< 0.1%

duration_ms
Real number (ℝ≥0)

Distinct70749
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236124.8184
Minimum15387
Maximum5552917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-12-04T12:38:35.768842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum15387
5-th percentile98644
Q1178286
median219467
Q3268530
95-th percentile413621.4
Maximum5552917
Range5537530
Interquartile range (IQR)90244

Descriptive statistics

Standard deviation130438.9707
Coefficient of variation (CV)0.5524153351
Kurtosis218.2302979
Mean236124.8184
Median Absolute Deviation (MAD)44706
Skewness9.451225297
Sum4.180566297 × 1010
Variance1.701432507 × 1010
MonotonicityNot monotonic
2021-12-04T12:38:35.860841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24000099
 
0.1%
19200090
 
0.1%
18000077
 
< 0.1%
21600070
 
< 0.1%
20000060
 
< 0.1%
21000056
 
< 0.1%
20800053
 
< 0.1%
18600053
 
< 0.1%
19800052
 
< 0.1%
23040049
 
< 0.1%
Other values (70739)176390
99.6%
ValueCountFrequency (%)
153871
< 0.1%
155091
< 0.1%
163161
< 0.1%
166401
< 0.1%
167481
< 0.1%
167601
< 0.1%
170001
< 0.1%
172131
< 0.1%
176271
< 0.1%
178401
< 0.1%
ValueCountFrequency (%)
55529171
< 0.1%
54880001
< 0.1%
48306061
< 0.1%
48305841
< 0.1%
48040151
< 0.1%
47917251
< 0.1%
46619911
< 0.1%
44979941
< 0.1%
43375291
< 0.1%
43033661
< 0.1%

energy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2517
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5572140445
Minimum2.03 × 10-5
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-12-04T12:38:35.949842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.03 × 10-5
5-th percentile0.0646
Q10.344
median0.592
Q30.789
95-th percentile0.947
Maximum0.999
Range0.9989797
Interquartile range (IQR)0.445

Descriptive statistics

Standard deviation0.2758135468
Coefficient of variation (CV)0.4949867103
Kurtosis-0.9829460586
Mean0.5572140445
Median Absolute Deviation (MAD)0.216
Skewness-0.3313174866
Sum98654.18937
Variance0.07607311261
MonotonicityNot monotonic
2021-12-04T12:38:36.042842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.721279
 
0.2%
0.843276
 
0.2%
0.72270
 
0.2%
0.859266
 
0.2%
0.73266
 
0.2%
0.686265
 
0.1%
0.675265
 
0.1%
0.714264
 
0.1%
0.676262
 
0.1%
0.728261
 
0.1%
Other values (2507)174375
98.5%
ValueCountFrequency (%)
2.03 × 10-51
< 0.1%
9.8 × 10-51
< 0.1%
0.0002161
< 0.1%
0.0002341
< 0.1%
0.0002432
< 0.1%
0.0002591
< 0.1%
0.0002632
< 0.1%
0.0002671
< 0.1%
0.0002731
< 0.1%
0.0004121
< 0.1%
ValueCountFrequency (%)
0.99917
 
< 0.1%
0.99844
 
< 0.1%
0.99756
 
< 0.1%
0.99696
0.1%
0.995141
0.1%
0.994117
0.1%
0.993124
0.1%
0.992125
0.1%
0.991136
0.1%
0.99168
0.1%

instrumentalness
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct5400
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1718749006
Minimum0
Maximum0.999
Zeros58286
Zeros (%)32.9%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-12-04T12:38:36.138841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.05 × 10-5
Q30.0901
95-th percentile0.911
Maximum0.999
Range0.999
Interquartile range (IQR)0.0901

Descriptive statistics

Standard deviation0.322779801
Coefficient of variation (CV)1.877992656
Kurtosis0.7307267337
Mean0.1718749006
Median Absolute Deviation (MAD)7.05 × 10-5
Skewness1.580423814
Sum30430.27928
Variance0.1041867999
MonotonicityNot monotonic
2021-12-04T12:38:36.238842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058286
32.9%
0.91207
 
0.1%
0.912206
 
0.1%
0.918205
 
0.1%
0.923200
 
0.1%
0.914199
 
0.1%
0.911196
 
0.1%
0.9194
 
0.1%
0.906194
 
0.1%
0.898193
 
0.1%
Other values (5390)116969
66.1%
ValueCountFrequency (%)
058286
32.9%
1 × 10-628
 
< 0.1%
1.01 × 10-670
 
< 0.1%
1.02 × 10-665
 
< 0.1%
1.03 × 10-653
 
< 0.1%
1.04 × 10-678
 
< 0.1%
1.05 × 10-672
 
< 0.1%
1.06 × 10-668
 
< 0.1%
1.07 × 10-662
 
< 0.1%
1.08 × 10-662
 
< 0.1%
ValueCountFrequency (%)
0.9991
 
< 0.1%
0.9981
 
< 0.1%
0.9972
 
< 0.1%
0.9963
 
< 0.1%
0.9946
< 0.1%
0.9938
< 0.1%
0.9929
< 0.1%
0.9919
< 0.1%
0.995
< 0.1%
0.98910
< 0.1%

key
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
C
21002 
G
20504 
D
18679 
A
17534 
C#
16880 
Other values (7)
82450 

Length

Max length2
Median length1
Mean length1.323955515
Min length1

Characters and Unicode

Total characters234405
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC#
2nd rowF#
3rd rowC
4th rowC#
5th rowF

Common Values

ValueCountFrequency (%)
C21002
11.9%
G20504
11.6%
D18679
10.6%
A17534
9.9%
C#16880
9.5%
F15629
8.8%
E13429
7.6%
B12916
7.3%
A#11935
6.7%
F#11384
6.4%
Other values (2)17157
9.7%

Length

2021-12-04T12:38:36.322841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c37882
21.4%
g31696
17.9%
a29469
16.6%
f27013
15.3%
d24644
13.9%
e13429
 
7.6%
b12916
 
7.3%

Most occurring characters

ValueCountFrequency (%)
#57356
24.5%
C37882
16.2%
G31696
13.5%
A29469
12.6%
F27013
11.5%
D24644
10.5%
E13429
 
5.7%
B12916
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter177049
75.5%
Other Punctuation57356
 
24.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C37882
21.4%
G31696
17.9%
A29469
16.6%
F27013
15.3%
D24644
13.9%
E13429
 
7.6%
B12916
 
7.3%
Other Punctuation
ValueCountFrequency (%)
#57356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin177049
75.5%
Common57356
 
24.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
C37882
21.4%
G31696
17.9%
A29469
16.6%
F27013
15.3%
D24644
13.9%
E13429
 
7.6%
B12916
 
7.3%
Common
ValueCountFrequency (%)
#57356
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII234405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
#57356
24.5%
C37882
16.2%
G31696
13.5%
A29469
12.6%
F27013
11.5%
D24644
10.5%
E13429
 
5.7%
B12916
 
5.5%

liveness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1732
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2244822076
Minimum0.00967
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-12-04T12:38:36.401844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.00967
5-th percentile0.0628
Q10.0975
median0.13
Q30.277
95-th percentile0.75
Maximum1
Range0.99033
Interquartile range (IQR)0.1795

Descriptive statistics

Standard deviation0.2109692111
Coefficient of variation (CV)0.9398037081
Kurtosis3.167265887
Mean0.2244822076
Median Absolute Deviation (MAD)0.049
Skewness1.957728131
Sum39744.35037
Variance0.04450800803
MonotonicityNot monotonic
2021-12-04T12:38:36.503841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1112107
 
1.2%
0.112007
 
1.1%
0.1081912
 
1.1%
0.1091867
 
1.1%
0.1071816
 
1.0%
0.1121810
 
1.0%
0.1061750
 
1.0%
0.1051732
 
1.0%
0.1041678
 
0.9%
0.1021617
 
0.9%
Other values (1722)158753
89.7%
ValueCountFrequency (%)
0.009671
< 0.1%
0.01021
< 0.1%
0.01051
< 0.1%
0.01191
< 0.1%
0.0121
< 0.1%
0.01211
< 0.1%
0.01231
< 0.1%
0.01242
< 0.1%
0.0131
< 0.1%
0.01361
< 0.1%
ValueCountFrequency (%)
15
< 0.1%
0.9991
 
< 0.1%
0.9983
 
< 0.1%
0.9971
 
< 0.1%
0.9969
< 0.1%
0.9954
 
< 0.1%
0.9944
 
< 0.1%
0.9938
< 0.1%
0.99210
< 0.1%
0.9917
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct27923
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-10.13355042
Minimum-52.457
Maximum3.744
Zeros2
Zeros (%)< 0.1%
Negative176963
Negative (%)> 99.9%
Memory size1.4 MiB
2021-12-04T12:38:36.596844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-52.457
5-th percentile-23.548
Q1-12.845
median-8.189
Q3-5.63
95-th percentile-3.3764
Maximum3.744
Range56.201
Interquartile range (IQR)7.215

Descriptive statistics

Standard deviation6.39299505
Coefficient of variation (CV)-0.6308741539
Kurtosis2.330035677
Mean-10.13355042
Median Absolute Deviation (MAD)3.123
Skewness-1.476240035
Sum-1794134.969
Variance40.87038571
MonotonicityNot monotonic
2021-12-04T12:38:36.679842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.42835
 
< 0.1%
-6.21735
 
< 0.1%
-5.73334
 
< 0.1%
-5.98234
 
< 0.1%
-4.80133
 
< 0.1%
-5.43933
 
< 0.1%
-5.01332
 
< 0.1%
-5.13132
 
< 0.1%
-6.28232
 
< 0.1%
-5.31832
 
< 0.1%
Other values (27913)176717
99.8%
ValueCountFrequency (%)
-52.4571
< 0.1%
-47.6691
< 0.1%
-47.5991
< 0.1%
-47.4991
< 0.1%
-47.4321
< 0.1%
-47.0461
< 0.1%
-46.9851
< 0.1%
-46.5071
< 0.1%
-46.1221
< 0.1%
-46.0521
< 0.1%
ValueCountFrequency (%)
3.7441
< 0.1%
1.9491
< 0.1%
1.8931
< 0.1%
1.611
< 0.1%
1.5851
< 0.1%
1.3421
< 0.1%
1.3141
< 0.1%
1.2751
< 0.1%
1.2581
< 0.1%
1.11
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Major
116793 
Minor
60256 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters885245
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMajor
2nd rowMinor
3rd rowMinor
4th rowMajor
5th rowMajor

Common Values

ValueCountFrequency (%)
Major116793
66.0%
Minor60256
34.0%

Length

2021-12-04T12:38:36.759841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-04T12:38:36.802842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
major116793
66.0%
minor60256
34.0%

Most occurring characters

ValueCountFrequency (%)
M177049
20.0%
o177049
20.0%
r177049
20.0%
a116793
13.2%
j116793
13.2%
i60256
 
6.8%
n60256
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter708196
80.0%
Uppercase Letter177049
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o177049
25.0%
r177049
25.0%
a116793
16.5%
j116793
16.5%
i60256
 
8.5%
n60256
 
8.5%
Uppercase Letter
ValueCountFrequency (%)
M177049
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin885245
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M177049
20.0%
o177049
20.0%
r177049
20.0%
a116793
13.2%
j116793
13.2%
i60256
 
6.8%
n60256
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII885245
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M177049
20.0%
o177049
20.0%
r177049
20.0%
a116793
13.2%
j116793
13.2%
i60256
 
6.8%
n60256
 
6.8%

speechiness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1641
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1273143576
Minimum0.0222
Maximum0.967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-12-04T12:38:36.861841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.0222
5-th percentile0.0286
Q10.0368
median0.0494
Q30.102
95-th percentile0.78
Maximum0.967
Range0.9448
Interquartile range (IQR)0.0652

Descriptive statistics

Standard deviation0.2042136762
Coefficient of variation (CV)1.604011362
Kurtosis8.977592316
Mean0.1273143576
Median Absolute Deviation (MAD)0.017
Skewness3.104766098
Sum22540.8797
Variance0.04170322555
MonotonicityNot monotonic
2021-12-04T12:38:36.954844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0363504
 
0.3%
0.0362503
 
0.3%
0.0374500
 
0.3%
0.0323491
 
0.3%
0.0337489
 
0.3%
0.0384480
 
0.3%
0.0343478
 
0.3%
0.0349477
 
0.3%
0.036473
 
0.3%
0.0341472
 
0.3%
Other values (1631)172182
97.3%
ValueCountFrequency (%)
0.02222
 
< 0.1%
0.02231
 
< 0.1%
0.02245
 
< 0.1%
0.02254
 
< 0.1%
0.02264
 
< 0.1%
0.02273
 
< 0.1%
0.022812
< 0.1%
0.02299
< 0.1%
0.0236
 
< 0.1%
0.023118
< 0.1%
ValueCountFrequency (%)
0.9671
 
< 0.1%
0.96511
 
< 0.1%
0.96413
 
< 0.1%
0.96321
 
< 0.1%
0.96242
< 0.1%
0.96141
< 0.1%
0.9671
< 0.1%
0.95959
< 0.1%
0.95873
< 0.1%
0.95792
0.1%

tempo
Real number (ℝ≥0)

Distinct78510
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.2146342
Minimum30.379
Maximum242.903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-12-04T12:38:37.043844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum30.379
5-th percentile74.0668
Q192.007
median115.009
Q3138.818
95-th percentile174.0836
Maximum242.903
Range212.524
Interquartile range (IQR)46.811

Descriptive statistics

Standard deviation31.32217844
Coefficient of variation (CV)0.2672207157
Kurtosis-0.4845495432
Mean117.2146342
Median Absolute Deviation (MAD)23.078
Skewness0.4064712033
Sum20752733.77
Variance981.0788619
MonotonicityNot monotonic
2021-12-04T12:38:37.131841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.00844
 
< 0.1%
120.00343
 
< 0.1%
12041
 
< 0.1%
120.01639
 
< 0.1%
120.00639
 
< 0.1%
119.99738
 
< 0.1%
119.98537
 
< 0.1%
120.00537
 
< 0.1%
119.99437
 
< 0.1%
120.01537
 
< 0.1%
Other values (78500)176657
99.8%
ValueCountFrequency (%)
30.3791
< 0.1%
31.0331
< 0.1%
31.6891
< 0.1%
31.9881
< 0.1%
32.081
< 0.1%
32.2441
< 0.1%
32.4511
< 0.1%
32.5091
< 0.1%
33.5931
< 0.1%
33.7921
< 0.1%
ValueCountFrequency (%)
242.9031
< 0.1%
239.8481
< 0.1%
236.7991
< 0.1%
236.7351
< 0.1%
235.4461
< 0.1%
234.9231
< 0.1%
232.691
< 0.1%
232.6021
< 0.1%
230.5121
< 0.1%
229.8861
< 0.1%

time_signature
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
4/4
149441 
3/4
20825 
5/4
 
4432
1/4
 
2345
0/4
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters531147
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4/4
2nd row4/4
3rd row5/4
4th row4/4
5th row4/4

Common Values

ValueCountFrequency (%)
4/4149441
84.4%
3/420825
 
11.8%
5/44432
 
2.5%
1/42345
 
1.3%
0/46
 
< 0.1%

Length

2021-12-04T12:38:37.209843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-04T12:38:37.259843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
4/4149441
84.4%
3/420825
 
11.8%
5/44432
 
2.5%
1/42345
 
1.3%
0/46
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4326490
61.5%
/177049
33.3%
320825
 
3.9%
54432
 
0.8%
12345
 
0.4%
06
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number354098
66.7%
Other Punctuation177049
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4326490
92.2%
320825
 
5.9%
54432
 
1.3%
12345
 
0.7%
06
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/177049
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common531147
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4326490
61.5%
/177049
33.3%
320825
 
3.9%
54432
 
0.8%
12345
 
0.4%
06
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII531147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4326490
61.5%
/177049
33.3%
320825
 
3.9%
54432
 
0.8%
12345
 
0.4%
06
 
< 0.1%

valence
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1692
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.451720923
Minimum0
Maximum1
Zeros28
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-12-04T12:38:37.331841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0416
Q10.222
median0.44
Q30.667
95-th percentile0.902
Maximum1
Range1
Interquartile range (IQR)0.445

Descriptive statistics

Standard deviation0.2677835157
Coefficient of variation (CV)0.5928074216
Kurtosis-1.072404618
Mean0.451720923
Median Absolute Deviation (MAD)0.222
Skewness0.1480027568
Sum79976.7377
Variance0.07170801127
MonotonicityNot monotonic
2021-12-04T12:38:37.423841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961397
 
0.2%
0.962332
 
0.2%
0.963301
 
0.2%
0.964292
 
0.2%
0.358263
 
0.1%
0.377246
 
0.1%
0.352244
 
0.1%
0.338243
 
0.1%
0.398241
 
0.1%
0.357240
 
0.1%
Other values (1682)174250
98.4%
ValueCountFrequency (%)
028
< 0.1%
0.01241
 
< 0.1%
0.01411
 
< 0.1%
0.01761
 
< 0.1%
0.01781
 
< 0.1%
0.0181
 
< 0.1%
0.01811
 
< 0.1%
0.01871
 
< 0.1%
0.01931
 
< 0.1%
0.02011
 
< 0.1%
ValueCountFrequency (%)
16
< 0.1%
0.9991
 
< 0.1%
0.9981
 
< 0.1%
0.9961
 
< 0.1%
0.9951
 
< 0.1%
0.9941
 
< 0.1%
0.9922
 
< 0.1%
0.9914
< 0.1%
0.995
< 0.1%
0.9899
< 0.1%

Interactions

2021-12-04T12:38:31.764846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:13.128852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:14.802850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:16.494851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:18.338849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:20.222848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:21.790849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:23.481846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:25.043848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:26.908847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:28.500844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:30.184845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:31.912847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:13.278852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:14.954853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:16.644851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:18.480848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:20.359850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:21.938848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:23.619847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:25.189847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:27.051861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:28.645848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:30.321847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:32.045845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:13.422852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:15.086849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:16.793849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:18.613848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:20.493848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:22.079849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:23.746846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:25.328845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:27.185846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:28.781845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:30.448846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:32.182844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:13.558851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:15.221851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:16.966853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:18.742849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:20.627851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:22.236849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:23.883848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:25.461845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:27.323844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:28.932843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:30.579843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:32.314842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:13.698850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:15.361850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:17.133848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:18.880851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:20.753848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:22.386847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:24.015846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:25.590847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:27.454845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:29.079845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:30.710846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:32.446846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:13.837850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:15.499851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:17.287852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:19.027849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:20.883848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:22.517848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:24.143847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:25.719848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:27.588848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:29.209848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:30.849844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:32.580844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:13.981851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:15.637851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:17.446849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:19.179848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:21.020848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:22.654848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:24.277847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:25.856846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:27.716845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:29.346843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:30.982846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:32.711845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:14.115853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:15.769852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:17.599848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:19.328849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:21.151850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:22.787848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:24.400847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:26.242846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:27.850844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:29.480847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:31.111846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:32.849842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:14.256851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:15.916850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:17.737850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:19.688850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:21.279850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:22.932847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:24.524846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:26.376846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:27.984845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:29.626847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:31.246844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:32.990845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:14.391850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:16.057849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:17.891851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:19.824848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:21.405848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:23.077847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:24.654849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:26.504844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:28.110845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:29.760844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:31.378844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:33.121843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:14.525850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:16.198850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:18.045849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:19.961850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:21.536850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:23.212847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:24.782847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:26.643848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:28.245847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:29.905844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:31.506843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:33.254843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:14.661849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:16.343848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:18.200848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:20.089847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:21.661848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:23.352846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:24.910847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:26.773845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:28.376847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:30.045846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-12-04T12:38:31.637846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-12-04T12:38:37.511844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-04T12:38:37.632841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-04T12:38:37.759843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-12-04T12:38:37.874842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-12-04T12:38:33.452842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-04T12:38:33.789841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indextrack_idartist_nametrack_namepopularityacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalence
000BRjO6ga9RKCKjfDqeFgWVHenri SalvadorC'est beau de faire un Show0.00.611000.389993730.91000.00000C#0.3460-1.828Major0.0525166.9694/40.814
110BjC1NfoEOOusryehmNudPMartin & les féesPerdu d'avance (par Gad Elmaleh)1.00.246000.5901373730.73700.00000F#0.1510-5.559Minor0.0868174.0034/40.816
220CoSDzoNIKCRs124s9uTVyJoseph WilliamsDon't Let Me Be Lonely Tonight3.00.952000.6631702670.13100.00000C0.1030-13.879Minor0.036299.4885/40.368
330Gc6TVm52BwZD07Ki6tIvfHenri SalvadorDis-moi Monsieur Gordon Cooper0.00.703000.2401524270.32600.00000C#0.0985-12.178Major0.0395171.7584/40.227
440IuslXpMROHdEPvSl1fTQKFabien NatafOuverture4.00.950000.331826250.22500.12300F0.2020-21.150Major0.0456140.5764/40.390
550Mf1jKa8eNAf1a4PwTbizjHenri SalvadorLe petit souper aux chandelles0.00.749000.5781606270.09480.00000C#0.1070-14.970Major0.143087.4794/40.358
660NUiKYRd6jt1LKMYGkUdnZMartin & les féesPremières recherches (par Paul Ventimila, Lorie Pester, Véronique Jannot, Michèle Laroque & Gérard Lenorman)2.00.344000.7032122930.27000.00000C#0.1050-12.675Major0.953082.8734/40.533
770PbIF9YVD505GutwotpB5CLaura MayneLet Me Let Go15.00.939000.4162400670.26900.00000F#0.1130-8.949Major0.028696.8274/40.274
880ST6uPfvaPpJLtQwhE6KfCChorusHelka0.00.001040.7342262000.48100.00086C0.0765-7.725Major0.0460125.0804/40.765
990VSqZ3KStsjcfERGdcWpFOLe Club des JuniorsLes bisous des bisounours10.00.319000.5981526940.70500.00125G0.3490-7.790Major0.0281137.4964/40.718

Last rows

df_indextrack_idartist_nametrack_namepopularityacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalence
1770391910421sQMwuozNCmF811OgjTWIAJoss StoneSome Kind Of Wonderful42.00.071400.8372361330.5410.000000C0.1520-5.068Major0.1360103.9714/40.654
1770401910436KkLg2UJB4sUIbtDyc8EsuThe KnocksComfortable (feat. X Ambassadors)41.00.001660.7642420130.7570.051700A#0.1020-7.049Minor0.0494115.0164/40.669
1770411910443UzHyDmqpBJR2J65h0702tGreat Good Fine OkYou're The One For Me38.00.038100.6302309370.6550.006060C0.1680-6.708Minor0.0366102.0094/40.329
1770421910450hZ8dTDpCuOgP0fZkBQEl1Keith SweatHow Many Ways39.00.581000.6592756710.6440.000000C#0.0701-4.510Major0.0848143.8874/40.646
1770431910462XoAEpBuM4AtQIQYUEowRyH-TownThey Like It Slow39.00.113000.6112795100.4930.000000C#0.1050-9.297Major0.0354115.9204/40.443
1770441910471U0OMWvR89Cm20vCNar50fJohn LegendQuickly (feat. Brandy)39.00.231000.7362226670.7010.000000A#0.2030-4.345Minor0.100099.9914/40.770
1770451910482gGqKJWfWbToha2YmDxnnjBellyP.O.P.43.00.104000.8022011730.5160.000485D0.1050-9.014Major0.2130175.6664/40.482
1770461910502iZf3EUedz9MPqbAvXdpdABobby "Blue" BlandI'll Take Care Of You - Single Version32.00.566000.4231446670.3370.000000A#0.2760-13.092Minor0.043680.0234/40.497
1770471910511qWZdkBl4UVPj9lK6HuuFMJr Thomas & The VolcanosBurning Fire38.00.032900.7852824470.6830.000880E0.2370-6.944Minor0.0337113.8304/40.969
17704819105334XO9RwPMKjbvRry54QzWnMint ConditionYou Don't Have To Hurt No More35.00.097300.7583230270.4700.000049G#0.0836-6.708Minor0.0287113.8974/40.479